When it comes to methods based on neural models, SDI [1]} and DCDI [2]}, are known to discover causal structure leveraging the combination of observational and interventional data for discrete and continuous data, respectively. Other approaches within this context, such as SAM, [3]}, only consider observational data. ENCO [4]} presents an efficient structure learning method for directed, acyclic causal graphs utilizing both observational and interventional data. They present the graph search as optimization of independent edge likelihoods, and reliably recover structures for large feature spaces.
